How to solve Cholesky decomposition error in Tensorflow caused by low precision datatype tf.float32?

Here’s a clean and structured format explaining how to solve the Cholesky decomposition error in TensorFlow due to tf.float32 precision issues:

Problem

Error:

InvalidArgumentError: Cholesky decomposition was not successful

Why?
Cholesky decomposition requires a symmetric, positive-definite matrix. Using tf.float32 can cause numerical instability, making the matrix not positive-definite due to rounding errors.

Solution Summary

Step	Fix	Code Example	Description
1	Use higher precision (tf.float64)	matrix = tf.cast(matrix, tf.float64)	Reduces numerical errors and improves stability.
2	Add jitter (small diagonal value)	matrix += tf.eye(n) * 1e-6	Helps ensure the matrix is numerically positive definite.
3	Ensure symmetry	matrix = (matrix + tf.transpose(matrix)) / 2	Eliminates minor asymmetry that might break decomposition.
4	Fallback: Use eigh or svd	tf.linalg.eigh(matrix)	Use spectral methods to analyse or fix the matrix.